Many professions require that practitioners and para-practitioners make judgments and decisions based upon or influenced by a complex interplay of information, factors and requirements from a range of sources and as the result of executing complex procedures which in themselves may involve complex and even conflicting requirements. A typical example is the medical profession, wherein a doctor or paramedical, such as a nurse practitioner, is required to acquire and consider a large volume of present and historical patient information and to decide, based on that information, whether to acquire further information and what procedures or methods to use in acquiring the additional information. The practitioner is then required to evaluate the patients present and probable future conditions and trends or developments, and to decide whether changes in treatment are necessary and what those changes should be. These processes are further complicated in that the practitioner is presented with a continuous flow or even flood of new and continuously changing information, recommendations and requirements.
For example, there are one or more professional associations or groups associated with virtually every significant medical condition or disease. Each of these groups or organizations is engaged in the study of the disease of interest and in the generation of recommendations and guidelines for the treatment of the disease, which change frequently as more is learned about the disease. The medical treatment industry, including pharmaceutical companies, medical equipment companies, hospitals and other medical treatment related enterprises are in turn engaged in the continuous development of new medications and methods for treatment of diseases or medical conditions, and recommendations for the use of the new medications or methods. Yet other organizations, such as the medical insurance organizations of various types, issue medical treatment guidelines based upon the guidelines developed by the professional organizations and medical industry and upon their own requirements and goals. These goals and requirements not only change continuously, but may conflict with the guidelines and recommendations of, for example, the professional organizations or those of other insurance organizations.
As a result, the practitioner is faced with increasingly complex decision making processes, involving increasing volumes and types of information and sources of information, increasing and continuously changing guidelines and requirements, increasing numbers of medications and methods for treatment, and increasingly numerous and more complex decision points in the processes for providing care to a patient. These problems are further compounded in that the guidelines or requirements of the various organizations often disagree or are in conflict. For example, a professional organization may recommend one medication for treatment of a condition, one insurance company may require a second medication, and another insurance company may approve only a third medication. These guidelines and requirements, however, are typically based upon generalized, statistical information gathered from studies and represent “average” patients and conditions. The guidelines also tend to be influenced by the specific interests of each group, such as health insurance or management organizations with a strong interest in cost containment. The doctor, however, is most familiar with the specific patient and the current condition and history of the patient, and may recognize that a different medication or course of treatment is preferable. The problem is still further compounded in that many of the groups and organizations supporting the practitioner, such as professional organizations, the pharmaceutical companies and the insurance companies, also request or require increasing volumes of reports from the practitioners, further increasing the workloads on the practitioners.
As a consequence, practitioners are often overwhelmed with a flood of information regarding each specific patient, the current and changing tests, guidelines, recommendations, medications and treatments for various diseases or conditions, conflict among the requirements or recommendations of various professional or service organizations, and the various reporting requirements or requests. As a result, and despite experience, thorough professional training and all due care on the part of the practitioner, it is possible for a practitioner to miss or forget a factor, a test, a possible medication or a requirement or a guideline simply because of the number of factors to consider for a given patient and the current range and complexity of possible medical procedures, even within a specific disease or condition. A practitioner may, for example, overlook or be unaware of indications of a developing condition, a precautionary or recommended test, a possible medication or medication conflict, a changed guideline, or avoidable conflicts with recommendations, guidelines or reporting requirements. For example, a newly changed guideline may warn that a change in or value of a blood test factor that was previously held to be insignificant is now regarded as a warning or indicator of a condition for which a precautionary test is recommended. In a further example, certain insurance companies may approve payment for specific medications but not for equivalent medications, thereby leading to possible conflicts with insurance company requirements that could be avoided.
Various practitioner support systems of the prior art, such as record generation/retrieval systems, information retrieval systems and “expert” systems, have attempted to address these problems. Such systems of the prior art have generally been of only limited success, however, because they either do not address or only partially address the actual needs and methods of practice of the practitioners.
For example, electronic medical record (EMR) systems are in common use to generate and retrieve on-line medical records for individual patients. Such EMR systems, however, do not assist the practitioner in performing medical examination and treatment processes, often referred to as “patient encounters”, but typically assist only by providing fast storage and retrieval of historical information pertaining to a patient. Because of the range and variety of medical information that could possibly be stored for a given patient, however, it is very difficult to create and maintain an electronic medical record having all of the necessary data storage fields for each patient and it is very difficult and time consuming to enter the medical data, such as test results and medications prescribed. As a result, EMR systems are often not used to their full potential. For example, many users attempt to implement paper record work flows in an EMR system, but fail to capture the true power of the EMR system, such as the digital storage of data which can be imported, exported, extracted and integrated to improve work flow and quality of care.
In a like manner, there are many on-line information retrieval systems available to the practitioner and through which a practitioner may search for and retrieve information pertaining to diagnostic symptoms, guidelines for treatment, medications and medication effects, insurance policies and requirements, and so on. While such information retrieval systems provide wide access to a vastly increased range of information, such systems are essentially merely substitutes for traditional hard copy references, such as the Merck manual. Again, such systems are too slow and cumbersome to be of assistance to the practitioner in real time patient encounters and many, if not most practitioners, tend to rely upon their experience and memory for such information during patient encounters or to refer to a hard copy of a reference work.
Lastly, there have been many attempts to create “expert” or “artificial intelligence” systems to aid medical practitioners, but such systems have been typically unsuccessful in practice for a number of fundamental reasons.
For example, “expert” systems which attempt to distill and provide the expertise of one or more experts in a given field are difficult to create because it is difficult, if not impossible, to insure that all of the required expert knowledge has in fact been extracted and embodied in the system. That is, and for example, a single expert may not have all of the desired knowledge, particularly in a large and complex field, and experts often disagree on essential matters, such as conditions for diagnosis and optimum treatment plans. In addition, human thought processes are extremely complex and are not well understood and experts often do not consciously understand how their minds reach a conclusion or retrieve a necessary bit of information. For example, a specific pattern of information may trigger an unconscious thought process and the retrieval of a critical bit of knowledge. The expert may be unaware of knowing that critical bit of knowledge on a conscious level, and the remembering may occur only for a very specific pattern of stimulus, so that it is virtually impossible to deliberately retrieve that information for inclusion in an expert system. For the same reasons, it is very difficult to maintain, update or correct such expert systems as knowledge and practices evolve.
While expert systems attempt to assist the practitioner by extending the practitioner's knowledge and, for example, analytical skills, “artificial intelligence” (AI) systems attempt to emulate the thought processes of the practitioner and to effectively either replace the practitioner or place a second practitioner at the human practitioner's shoulder. Artificial intelligence systems, however, not only have all of the problems of expert systems with regard to extracting and embodying knowledge, but also have unique problems that limit their use in many fields. For example, many AI systems are designed for and capable of “learning” or self-modification over time and with “experience”. While this is one of the advantages and desired features of AI systems, it can be a problem in many applications due to uncertainty over time with respect to the rules, principles and information through which and upon which a system is currently providing advice or decisions. For these reasons, AI systems are often regarded as too uncertain or unreliable for certain applications, such as medical support services. There are also psychological problems with AI systems as practitioners are uncomfortable with systems that apparently attempt to replace the practitioners, or to at least displace or “second guess” their primary function, and patients are often uncomfortable dealing solely or primarily with a computer system rather than a human in medical matters.
The present invention provides a solution to these and other related problems of the prior art.